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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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 "cells": [
  {
   "source": [
    "机器学习中，线性模型需要用到非线性数据时，需要对非线性数据进行分箱。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "source": [
    "### 1. 绘制散点图函数plt.scatter"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.random.randn(10)\n",
    "x2 = x1 + x1 ** 2 - 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 确定画布，比例为8:4\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.scatter(x1,x2,s=50,c='red',label='Red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "### 2.散点图多个颜色与图例"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x288 with 1 Axes>",
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Dk1yhiIhIvXjc1hVtF9WbTP8NGOruR8xsOvA0EPWeKTMrAAoAhgwZEofyWjb206N5eOu9PLf4Rbb9bScDhvVjxnVTGHpafoePLSIi0lbm3jRb27kAs/OB77n71Mj0bQDu/tMWfqcICLv7/paWHQ6HvbCwMKb6REREgsLM1rp7OFpbPA6JrwFGmNlwM8sGZgPLmhQwwCIni81sfGTc0jiMLSIikhZiPiTu7jVmdgPwAhACHnL3jWZ2XaR9EfBF4HozqwHKgdke6669iIhIGon5kHhH0iFxERFJJx19SFxEREQ6mAJbREQkABTYIiIiAaDAFhERCQAFtoiISAAosEVEpF3ca3E/8bXE0rEU2CIi0iZetYa6/V/A947C955O3YECvOYfyS4rbcTjWeIiceG1pXj5b6GqEDL6Yl2/gmWfneyyRATwyr/gH1wPVETm1ELVn/HStdDnKSyz49/9kO4U2NIpePUG/MBV4NVAJWB4xQt41y+TcdKCZJcnktbcHT/0ff4Z1g0t4EfxI/divRYmo7S0okPiknTujn8wH/wI9WEN9S98K4eyJ/DK/01idSJC3X6o3d1cI1S+nNBy0pUCW5Kv+u/gh5ppLMfLHk1oOSIinZECW5Kvbj/RX6seUbs3YaWISBQZfSE0qLlGyLkooeWkKwW2JF/mqZFz19FkQfZZCS1HRI5nZthJ3wFym7aAdcO635iMstKOAluSzjKHQnYYyI7Smol1nZPokkSkCcuZiPX+L8gcTf0RsRBk/wvW53e6QjxBdJW4dArW6x78g+ugej1YBvV/EDKwXr/QHwORTsKyx2N9/wf3GiADM+3zJZICWzoFy+iB9VmKV2+Fmo1gvSBnImbR9rpFJJnMFB3JEJd/HpnZNDPbYmbbzezWKO1mZvdE2teZ2bh4jCupx7I+iXW5FMu9UGEtItJIzIFtZiHgPuBiYBRwmZmNatLtYmBE5FMA3B/ruCIiIukkHnvY44Ht7r7T3auAx4FZTfrMApZ4vdeBXmY2MA5ji4iIpIV4BPYgYFej6eLIvPb2ERERkWbEI7CjPfHCP0Kf+o5mBWZWaGaFJSUlMRcnIiKSCuIR2MXA4EbT+UDTh862pQ8A7r7Y3cPuHs7Ly4tDeSIiIsEXj8BeA4wws+FWf1nvbGBZkz7LgKsiV4ufBxx09z1xGFtERCQtxHwznbvXmNkNwAtACHjI3Tea2XWR9kXAcmA6sB0oA+bFOq6IiEg6icvd7+6+nPpQbjxvUaPvDsyPx1giIiLpSM+VExERCYCUf76cex1U/QWv/AtYLpb7WSxrRLLLEhERaZeUDmyvO4wfuAJq3wEvA0L40YfwLpdiJ30PsxbewSwiItKJpPQhcT+4AGp2RMIaoBaogPKnoeLZZJYmIiLSLikb2F53GCr/BFRFaS3Hjy5OdEkiIiIfWcoGNnX7wLKab699P3G1iIiIxCh1AzujP3h18+0hPcpcRESCI2UD2zK6Q+40IMo7la0L1v3ahNckIiLyUaVsYAPYSd+HrFFgXal//0gWkANd/hVypiW5OhERkbZL6du6LKMb9P4tVK+FqjfAciFnCpY5uPVfFhER6URSOrCB+nuts8P1HxERkYBK6UPiIiIiqUKBLSIiEgAKbBERkQBQYIuIiARATBedmVlv4LfAMKAI+LK7fxClXxFwmPqHede4u64AExERaYdY97BvBV529xHAy5Hp5lzo7mMU1iIiIu0Xa2DPAh6JfH8EuCTG5YmIiEgUsQZ2f3ffAxD52a+Zfg6sMLO1ZlYQ45giIiJpp9Vz2Gb2EjAgStOCdowz0d13m1k/4EUz2+zuK5sZrwAoABgyZEg7hhAREUldrQa2u09urs3M9prZQHffY2YDgX3NLGN35Oc+M3sKGA9EDWx3XwwsBgiHw976KoiIiKS+WA+JLwPmRL7PAZ5p2sHMuplZj2PfgSnAhhjHFRERSSuxBvYdwGfMbBvwmcg0ZnaKmS2P9OkPrDKzt4DVwHPu/scYxxUREUkrMd2H7e6lwEVR5u8Gpke+7wTOimUcERGRdKcnnYmIiASAAltERCQAFNgiIiIBoMAWEREJAAW2iIhIACiwRUREAkCBLSIiEgAKbBERkQBQYIuIiASAAltERCQAFNgiIiIBoMAWEREJAAW2iIhIACiwRUREAkCBLSIiEgAKbBERkQCIKbDN7EtmttHM6sws3EK/aWa2xcy2m9mtsYwpIiKSjmLdw94AfB5Y2VwHMwsB9wEXA6OAy8xsVIzjioiIpJXMWH7Z3TcBmFlL3cYD2919Z6Tv48As4O1YxhYREUkniTiHPQjY1Wi6ODIvKjMrMLNCMyssKSnp8OJERESCoNU9bDN7CRgQpWmBuz/ThjGi7X57c53dfTGwGCAcDjfbT0REJJ20GtjuPjnGMYqBwY2m84HdMS5TREQkrSTikPgaYISZDTezbGA2sCwB44qIiKSMWG/rutTMioHzgefM7IXI/FPMbDmAu9cANwAvAJuAJ9x9Y2xli4jEbteW9/jR7J8zq9ccLu0zl58VLKKkuDTZZYlEZe6d9zRxOBz2wsLCZJchIilox1tFfPNT36airBKvq/87GMoM0a1nF+5fexf9huQluUJJR2a21t2jPtdETzoTkbR07w0PUn6koiGsAWprajnyYRm//vbjSaxMJDoFtoiknbLD5WxevS1qW11tHauefCPBFYm0ToEtImmntqa2xQc+1dbUJrAakbZRYItI2uneqxv9hzZ/jnr0p/T0ZOl8FNgiknbMjOt/NoecrtkntOV0zebqn1yehKpEWqbAFpG0dO5nz+a2R79B/6F5ZOdmkZWTxdDTB/PT5/8fp4Y/nuzyRE4Q08s/RESCbOIl45kw6xxKdx8gI5RB7wEnJ7skkWYpsEUkrZkZfQf1SXYZIq3SIXEREZEAUGCLiIgEgAJbREQkABTYIiIiAaDAFhERCQAFtoiISAAosEVERAIgpsA2sy+Z2UYzqzOzqO/vjPQrMrP1ZvammekF1yIiIu0U64NTNgCfBx5oQ98L3X1/jOOJiEgc7N7xPv9Y/y69B57MyPGfaPHtZdI5xBTY7r4J0IYWEQmIIx8e5QdfupuNf91CZlYmXldHjz7d+f5Tt/CJMcOTXZ60IFHnsB1YYWZrzawgQWOKiEgT3555B+tf20RVeRVlh8ooP1LBvnf2838v/B6HSg8nuzxpQauBbWYvmdmGKJ9Z7RhnoruPAy4G5pvZpBbGKzCzQjMrLCkpaccQIiLSkp3r3mHb2p3UVNWc0FZTVcPy/3o5CVVJW7Ua2O4+2d3PiPJ5pq2DuPvuyM99wFPA+Bb6Lnb3sLuH8/Kaf8G8iIi0z9a1O7GM6KcwK8urWL/y7QRXJO3R4YfEzaybmfU49h2YQv3FaiIikkAn9e5ORkb0P/uWYZw8oFeCK5L2iPW2rkvNrBg4H3jOzF6IzD/FzJZHuvUHVpnZW8Bq4Dl3/2Ms44qISPuFp42BZq4Rzs7NZvo1kxNbkLRLTIHt7k+5e76757h7f3efGpm/292nR77vdPezIp/T3f3H8ShcRETaJzsniwWPfZOcrjmEskIN83O75TDj2smMOu+TSaxOWhPrfdgiIhIg4y8ey6K/L+SpXzzH5tXbycvvzawbLmbsp0cnuzRphQJbRCTN5I8YyI2//Gqyy5B20rPERUREAkCBLSIiEgAKbBERkQBQYIuIiASAAltERCQAFNgiIiIBoMAWEREJAAW2iIhIACiwRUREAkCBLSIiEgAKbBERkQBQYIuIiASAAltERCQAYgpsM1toZpvNbJ2ZPWVmvZrpN83MtpjZdjO7NZYxRURE0lGse9gvAme4+5nAVuC2ph3MLATcB1wMjAIuM7NRMY4rIiKSVmIKbHdf4e41kcnXgfwo3cYD2919p7tXAY8Ds2IZV0REJN3E8xz21cDzUeYPAnY1mi6OzBMREZE2ymytg5m9BAyI0rTA3Z+J9FkA1ABLoy0iyjxvYbwCoABgyJAhrZUnIiKSFloNbHef3FK7mc0BZgAXuXu0IC4GBjeazgd2tzDeYmAxQDgcbjbYRURE0kmsV4lPA74FzHT3sma6rQFGmNlwM8sGZgPLYhlXREQk3cR6DvuXQA/gRTN708wWAZjZKWa2HCByUdoNwAvAJuAJd98Y47giIiJppdVD4i1x9080M383ML3R9HJgeSxjiYiIpDM96UxERCQAFNgichx358D7H3D4gyPJLkVEGonpkLiIpJY/P/FXHrh5CR+WHMLrnI+PGcZNiwr4xJjhyS5NJO1pD1tEAPjTY6+x8Or7KNlVSnVFNTVVNWxZvZ1/n/Qddm15L9nliaQ9BbaIUFdXxwP/sYTKsqoT2irLKnn0h79PQlUi0pgCW0TY+04JRw9Ff5RCXZ2z5vm/J7giEWlKgS0iZGZl4nXNP1gwlKXLXUSSTYEtIuTl96H/sH5R2zKzQlw4e2KCKxKRphTYIgLANx+4lpyu2Vij1/WEMkP06N2dy267NHmFiQigwBaRiNGfOo2fr/wh4aljyemaQ/de3bj43z7Nor8v5OT+vZJdnkjas+gv2OocwuGwFxYWJrsMERGRhDCzte4ejtamPWwREZEAUGCLiIgEgAJbREQkABTYIiIiAaDAFhERCYBOfZW4mZUA78S4mL7A/jiUE0Ra9/SVzuuvdU9PqbLuQ909L1pDpw7seDCzwuYukU91Wvf0XHdI7/XXumvdU5UOiYuIiASAAltERCQA0iGwFye7gCTSuqevdF5/rXt6Svl1T/lz2CIiIqkgHfawRUREAi/lAtvMFprZZjNbZ2ZPmVnU1wyZ2TQz22Jm283s1kTX2RHM7EtmttHM6sys2aslzazIzNab2ZtmlhJvV2nHuqfidu9tZi+a2bbIz5Ob6Zcy27217Wj17om0rzOzccmosyO0Yd0vMLODke38ppl9Jxl1dgQze8jM9pnZhmbaU3a7A+DuKfUBpgCZke93AndG6RMCdgAfA7KBt4BRya49Dut+GnAq8CoQbqFfEdA32fUmet1TeLvfBdwa+X5rtP/mU2m7t2U7AtOB5wEDzgPeSHbdCVz3C4Bnk11rB63/JGAcsKGZ9pTc7sc+KbeH7e4r3L0mMvk6kB+l23hgu7vvdPcq4HFgVqJq7CjuvsndtyS7jmRo47qn5Hanfh0eiXx/BLgkibUkQlu24yxgidd7HehlZgMTXWgHSNX/htvE3VcCB1rokqrbHUjBQ+JNXE39v7aaGgTsajRdHJmXLhxYYWZrzawg2cUkUKpu9/7uvgcg8rNfM/1SZbu3ZTum6rZu63qdb2ZvmdnzZnZ6YkrrFFJ1uwOQmewCPgozewkYEKVpgbs/E+mzAKgBlkZbRJR5gbhcvi3r3gYT3X23mfUDXjSzzZF/uXZqcVj3lNzu7VhMILd7FG3ZjoHd1q1oy3r9jfrHWx4xs+nA08CIDq+sc0jV7Q4ENLDdfXJL7WY2B5gBXOSRExtNFAODG03nA7vjV2HHaW3d27iM3ZGf+8zsKeoPs3X6P9xxWPeU3O5mttfMBrr7nsjhv33NLCOQ2z2KtmzHwG7rVrS6Xu5+qNH35Wb2KzPr6+6p8Jzt1qTqdgdS8JC4mU0DvgXMdPeyZrqtAUaY2XAzywZmA8sSVWMymVk3M+tx7Dv1F+lFveIyBaXqdl8GzIl8nwOccLQhxbZ7W7bjMuCqyFXD5wEHj502CLhW193MBpiZRb6Pp/7vfGnCK02OVN3u9ZJ91Vu8P8B26s9hvBn5LIrMPwVY3qjfdGAr9VdcLkh23XFa90up/xdmJbAXeKHpulN/delbkc/GdFr3FN7ufYCXgW2Rn71TfbtH247AdcB1ke8G3BdpX08Ld00E7dOGdb8hso3fov7C2wnJrjmO6/4YsAeojvz//d/SZbu7u550JiIiEgQpd0hcREQkFSmwRUREAkCBLSIiEgAKbBERkQBQYIuIiASAAltERCQAFNgiIiIBoMAWEREJgP8PNEWa3oLR8AEAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "X = np.random.randn(10,2)\n",
    "y = np.array([0,0,1,1,0,1,0,1,0,0])\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.scatter(X[:,0], X[:,1]\n",
    "            ,s=50\n",
    "            ,c=y\n",
    "            ,label = ['Zero', 'One']\n",
    "            )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "#### 【核心知识点】可视化分类标签时的图例\n",
    "\n",
    "如果我们希望显示多种颜色的散点图，并且这个颜色是我们的标签y所代表的分类，那我们**无法**让散点图显示分别代表不同颜色的图例<br>\n",
    "\n",
    "解决方法：在同一个画布上循环画图，指定不同的图例"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "colors = ['red', 'black']\n",
    "labels = ['Zero', 'One']\n",
    "\n",
    "for i in range(X.shape[1]):\n",
    "    plt.scatter(X[y == i, 0]    # 这里的y是标签，用布尔值的方式取数\n",
    "                ,X[y == i, 1]\n",
    "                ,c = colors[i]\n",
    "                ,label = labels[i]\n",
    "                )\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "#### 总结一下：\n",
    "画散点图需要找到三个元素：\n",
    "\n",
    "1. 数据x1和x2<br>\n",
    "2. 标签的列表<br>\n",
    "3. 颜色列表\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 3. 认识下绘图用到的数据"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入提供的数据\n",
    "midwest = pd.read_csv(\"data/midwest_filter.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(332, 29)"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "midwest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   PID     county state   area  poptotal   popdensity  popwhite  popblack  \\\n",
       "0  561      ADAMS    IL  0.052     66090  1270.961540     63917      1702   \n",
       "1  562  ALEXANDER    IL  0.014     10626   759.000000      7054      3496   \n",
       "2  563       BOND    IL  0.022     14991   681.409091     14477       429   \n",
       "3  564      BOONE    IL  0.017     30806  1812.117650     29344       127   \n",
       "4  565      BROWN    IL  0.018      5836   324.222222      5264       547   \n",
       "\n",
       "   popamerindian  popasian  ...  percprof  poppovertyknown  percpovertyknown  \\\n",
       "0             98       249  ...  4.355859            63628         96.274777   \n",
       "1             19        48  ...  2.870315            10529         99.087145   \n",
       "2             35        16  ...  4.488572            14235         94.956974   \n",
       "3             46       150  ...  4.197800            30337         98.477569   \n",
       "4             14         5  ...  3.367680             4815         82.505140   \n",
       "\n",
       "   percbelowpoverty  percchildbelowpovert  percadultpoverty  \\\n",
       "0         13.151443             18.011717         11.009776   \n",
       "1         32.244278             45.826514         27.385647   \n",
       "2         12.068844             14.036061         10.852090   \n",
       "3          7.209019             11.179536          5.536013   \n",
       "4         13.520249             13.022889         11.143211   \n",
       "\n",
       "   percelderlypoverty  inmetro  category    dot_size  \n",
       "0           12.443812        0       AAR  250.944411  \n",
       "1           25.228976        0       LHR  185.781260  \n",
       "2           12.697410        0       AAR  175.905385  \n",
       "3            6.217047        1       ALU  319.823487  \n",
       "4           19.200000        0       AAR  130.442161  \n",
       "\n",
       "[5 rows x 29 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PID</th>\n      <th>county</th>\n      <th>state</th>\n      <th>area</th>\n      <th>poptotal</th>\n      <th>popdensity</th>\n      <th>popwhite</th>\n      <th>popblack</th>\n      <th>popamerindian</th>\n      <th>popasian</th>\n      <th>...</th>\n      <th>percprof</th>\n      <th>poppovertyknown</th>\n      <th>percpovertyknown</th>\n      <th>percbelowpoverty</th>\n      <th>percchildbelowpovert</th>\n      <th>percadultpoverty</th>\n      <th>percelderlypoverty</th>\n      <th>inmetro</th>\n      <th>category</th>\n      <th>dot_size</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>561</td>\n      <td>ADAMS</td>\n      <td>IL</td>\n      <td>0.052</td>\n      <td>66090</td>\n      <td>1270.961540</td>\n      <td>63917</td>\n      <td>1702</td>\n      <td>98</td>\n      <td>249</td>\n      <td>...</td>\n      <td>4.355859</td>\n      <td>63628</td>\n      <td>96.274777</td>\n      <td>13.151443</td>\n      <td>18.011717</td>\n      <td>11.009776</td>\n      <td>12.443812</td>\n      <td>0</td>\n      <td>AAR</td>\n      <td>250.944411</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>562</td>\n      <td>ALEXANDER</td>\n      <td>IL</td>\n      <td>0.014</td>\n      <td>10626</td>\n      <td>759.000000</td>\n      <td>7054</td>\n      <td>3496</td>\n      <td>19</td>\n      <td>48</td>\n      <td>...</td>\n      <td>2.870315</td>\n      <td>10529</td>\n      <td>99.087145</td>\n      <td>32.244278</td>\n      <td>45.826514</td>\n      <td>27.385647</td>\n      <td>25.228976</td>\n      <td>0</td>\n      <td>LHR</td>\n      <td>185.781260</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>563</td>\n      <td>BOND</td>\n      <td>IL</td>\n      <td>0.022</td>\n      <td>14991</td>\n      <td>681.409091</td>\n      <td>14477</td>\n      <td>429</td>\n      <td>35</td>\n      <td>16</td>\n      <td>...</td>\n      <td>4.488572</td>\n      <td>14235</td>\n      <td>94.956974</td>\n      <td>12.068844</td>\n      <td>14.036061</td>\n      <td>10.852090</td>\n      <td>12.697410</td>\n      <td>0</td>\n      <td>AAR</td>\n      <td>175.905385</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>564</td>\n      <td>BOONE</td>\n      <td>IL</td>\n      <td>0.017</td>\n      <td>30806</td>\n      <td>1812.117650</td>\n      <td>29344</td>\n      <td>127</td>\n      <td>46</td>\n      <td>150</td>\n      <td>...</td>\n      <td>4.197800</td>\n      <td>30337</td>\n      <td>98.477569</td>\n      <td>7.209019</td>\n      <td>11.179536</td>\n      <td>5.536013</td>\n      <td>6.217047</td>\n      <td>1</td>\n      <td>ALU</td>\n      <td>319.823487</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>565</td>\n      <td>BROWN</td>\n      <td>IL</td>\n      <td>0.018</td>\n      <td>5836</td>\n      <td>324.222222</td>\n      <td>5264</td>\n      <td>547</td>\n      <td>14</td>\n      <td>5</td>\n      <td>...</td>\n      <td>3.367680</td>\n      <td>4815</td>\n      <td>82.505140</td>\n      <td>13.520249</td>\n      <td>13.022889</td>\n      <td>11.143211</td>\n      <td>19.200000</td>\n      <td>0</td>\n      <td>AAR</td>\n      <td>130.442161</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 29 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "midwest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['PID', 'county', 'state', 'area', 'poptotal', 'popdensity', 'popwhite',\n",
       "       'popblack', 'popamerindian', 'popasian', 'popother', 'percwhite',\n",
       "       'percblack', 'percamerindan', 'percasian', 'percother', 'popadults',\n",
       "       'perchsd', 'percollege', 'percprof', 'poppovertyknown',\n",
       "       'percpovertyknown', 'percbelowpoverty', 'percchildbelowpovert',\n",
       "       'percadultpoverty', 'percelderlypoverty', 'inmetro', 'category',\n",
       "       'dot_size'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "midwest.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['城市ID',\n",
       " '郡',\n",
       " '州',\n",
       " '面积',\n",
       " '总人口',\n",
       " '人口密度',\n",
       " '白人人口',\n",
       " '非裔人口',\n",
       " '美洲印第安人人口',\n",
       " '亚洲人口',\n",
       " '其他人种人口',\n",
       " '白人所占比例',\n",
       " '非裔所占比例',\n",
       " '美洲印第安人所占比例',\n",
       " '亚洲人所占比例',\n",
       " '其他人种比例',\n",
       " '成年人口',\n",
       " '具有高中文凭的比率',\n",
       " '大学文凭比例',\n",
       " '有工作的人群比例',\n",
       " '已知贫困人口',\n",
       " '已知贫困人口的比例',\n",
       " '贫困线以下的人的比例',\n",
       " '贫困线以下的儿童所占比例',\n",
       " '贫困的成年人所占的比例',\n",
       " '贫困的老年人所占的比例',\n",
       " '是否拥有地铁',\n",
       " '标签',\n",
       " '点的尺寸']"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "#给大家翻译了一下：\n",
    "[\"城市ID\",\"郡\",\"州\",\"面积\",\"总人口\",\"人口密度\",\"白人人口\",\"非裔人口\",\"美洲印第安人人口\",\"亚洲人口\",\"其他人种人口\"\n",
    ",\"白人所占比例\",\"非裔所占比例\",\"美洲印第安人所占比例\",\"亚洲人所占比例\",\"其他人种比例\"\n",
    ",\"成年人口\",\"具有高中文凭的比率\",\"大学文凭比例\",\"有工作的人群比例\"\n",
    ",\"已知贫困人口\",\"已知贫困人口的比例\",\"贫困线以下的人的比例\",\"贫困线以下的儿童所占比例\",\"贫困的成年人所占的比例\",\"贫困的老年人所占的比例\"\n",
    ",\"是否拥有地铁\",\"标签\",\"点的尺寸\"]"
   ]
  },
  {
   "source": [
    "### 4. 准备标签列表和颜色列表"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "#### 标签"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# midwest['category'].unqiue() 'Series' object has no attribute 'unqiue'\n",
    "\n",
    "categories = np.unique(midwest['category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array(['AAR', 'AAU', 'AHR', 'ALR', 'ALU', 'HAR', 'HAU', 'HHR', 'HLR',\n",
       "       'HLU', 'LAR', 'LAU', 'LHR', 'LHU'], dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "categories"
   ]
  },
  {
   "source": [
    "#### 颜色"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "面对有特别多颜色的需求时，可以使用matplotlib自带的方法来自动生成颜色<br>\n",
    "众多光谱可以参考：https://matplotlib.org/tutorials/colors/colormaps.html<br>\n",
    "其中，最常用的是`plt.cm.tab10()`，在其中输入任意浮点数，就能提取出一种颜色出来<br>\n",
    "光谱tab10中总共只有十种颜色，如果输入的浮点数比较接近，会返回类似的颜色<br>\n",
    "这种颜色会以元祖的形式返回，表示为四个浮点数组成的RGBA色彩空间或者三个浮点数组成的RGB色彩空间中的随机色彩<br>\n",
    "A指透明度，这个方法给出的颜色透明度都为1\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "color1 = plt.cm.tab10(5.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0.09019607843137255, 0.7450980392156863, 0.8117647058823529, 1.0)"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "color1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "x1 = np.random.randn(10)\n",
    "x2 = x1 + x1 ** 2 - 10\n",
    "plt.scatter(x1,x2,s=50,c=color1)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "> *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
    "\n",
    "这里的警告大概意思是，颜色的值最好是二维数组，如果是一维数组，搞不清楚是指用一个颜色还是多个颜色"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0.09019608, 0.74509804, 0.81176471, 1.        ]])"
      ]
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "# reshape：增维，输入(1,-1)是让行上的维度为1，(-1,1)是让列上的维度为1\n",
    "np.array(color1).reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "x1 = np.random.randn(10)\n",
    "x2 = x1 + x1 ** 2 - 10\n",
    "plt.scatter(x1,x2,s=50,c=np.array(color1).reshape(1,-1))\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "### 5.生成基础的图像"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    66090\n",
       "2    14991\n",
       "4     5836\n",
       "5    35688\n",
       "7    16805\n",
       "Name: poptotal, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "# 取选定类别的指定列\n",
    "midwest.loc[midwest['category'] == categories[0], 'poptotal'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1920x1200 with 1 Axes>",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "# 为了标签的名称，我们本来就需要循环和标签的类别数目一样的次数\n",
    "# 所以需要循环的次数 = 需要生成的颜色的次数 = 需要生成的小数的个数\n",
    "# 只需要在循环中不断生成颜色就好了\n",
    "plt.figure(\n",
    "            figsize=(16,10)     #绘图尺寸\n",
    "            ,dpi=120            #图像分辨率\n",
    "            ,facecolor='w'      #图像的背景颜色，设置为白色，默认也是白色\n",
    "            ,edgecolor='w'      #图像的边框颜色，设置为白色，就能去掉坐标轴\n",
    ")\n",
    "\n",
    "for i in range(len(categories)):\n",
    "    plt.scatter(midwest.loc[midwest['category'] == categories[i], 'area']\n",
    "                ,midwest.loc[midwest['category'] == categories[i], 'poptotal']\n",
    "                ,s=20\n",
    "                ,c=np.array(plt.cm.tab10(i/len(categories))).reshape(1,-1)\n",
    "                ,label = categories[i]\n",
    "                )\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "source": [
    "### 6.丰富图像（原博客代码分析）"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "设置图像属性，在notebook关闭之前都有效，关闭后无效，需要在每个notebook开始处进行设置"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置各种属性\n",
    "large = 22\n",
    "med = 16\n",
    "small = 12\n",
    "\n",
    "params = {'axes.titlesize': large, #子图上的标题字体大小\n",
    "          'legend.fontsize': med, #图例的字体大小\n",
    "          'figure.figsize': (16, 10), #图像的画布大小\n",
    "          'axes.labelsize': med, #标签的字体大小\n",
    "          'xtick.labelsize': med, #x轴上的标尺的字体大小\n",
    "          'ytick.labelsize': med, #y轴上的标尺的字体大小\n",
    "          'figure.titlesize': large} #整个画布的标题字体大小\n",
    "plt.rcParams.update(params) #设定各种各样的默认属性\n",
    "plt.style.use('seaborn-whitegrid') #设定整体风格\n",
    "sns.set_style(\"white\") #设定整体背景风格\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 准备标签列表和颜色标签\n",
    "categories = np.unique(midwest['category'])\n",
    "colors = [plt.cm.tab10(i/float(len(categories) - 1)) for i in range(len(categories))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "# 建立画布,这里对画布进行设置会改变预设值的内容\n",
    "plt.figure(\n",
    "            figsize=(16,10)     #绘图尺寸\n",
    "            ,dpi=120            #图像分辨率\n",
    "            ,facecolor='w'      #图像的背景颜色，设置为白色，默认也是白色\n",
    "            ,edgecolor='k'      #图像的边框颜色，设置为黑色，默认也是黑色\n",
    ")\n",
    "\n",
    "# 循环画图\n",
    "for i, category in enumerate(categories):\n",
    "    plt.scatter('area', 'poptotal'      # 这里采用传入横纵坐标的名字，然后指定data来传入所有数据\n",
    "                ,data = midwest.loc[midwest.category == category, :]\n",
    "                ,s = 20\n",
    "                ,c = np.array(colors[i]).reshape(1,-1)\n",
    "                ,label = str(category)\n",
    "                )\n",
    "\n",
    "# 对图像进行装饰\n",
    "# plt.gca() 获取当前子图，没有当前子图则创建一个\n",
    "plt.gca().set(xlim = (0,0.12), ylim = (0, 80000))   # 控制坐标轴范围\n",
    "plt.xticks(fontsize=12) # 坐标轴上字体大小\n",
    "plt.yticks(fontsize=12)\n",
    "plt.ylabel('Population', fontsize=22)\n",
    "plt.xlabel('Area', fontsize=22)\n",
    "plt.title(\"Scatterplot of Midwest Area vs Population\", fontsize=22)\n",
    "plt.legend(fontsize=12,loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "#### 7. 用逻辑回归探索类型是什么"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "#### 数据预处理"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分出来category\n",
    "midwest['c1'] = midwest['category'].apply(lambda x: x[0])\n",
    "midwest['c2'] = midwest['category'].apply(lambda x: x[1])\n",
    "midwest['c3'] = midwest['category'].apply(lambda x: x[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   PID     county state   area  poptotal   popdensity  popwhite  popblack  \\\n",
       "0  561      ADAMS    IL  0.052     66090  1270.961540     63917      1702   \n",
       "1  562  ALEXANDER    IL  0.014     10626   759.000000      7054      3496   \n",
       "2  563       BOND    IL  0.022     14991   681.409091     14477       429   \n",
       "3  564      BOONE    IL  0.017     30806  1812.117650     29344       127   \n",
       "4  565      BROWN    IL  0.018      5836   324.222222      5264       547   \n",
       "\n",
       "   popamerindian  popasian  ...  percbelowpoverty  percchildbelowpovert  \\\n",
       "0             98       249  ...         13.151443             18.011717   \n",
       "1             19        48  ...         32.244278             45.826514   \n",
       "2             35        16  ...         12.068844             14.036061   \n",
       "3             46       150  ...          7.209019             11.179536   \n",
       "4             14         5  ...         13.520249             13.022889   \n",
       "\n",
       "   percadultpoverty  percelderlypoverty  inmetro  category    dot_size  c1  \\\n",
       "0         11.009776           12.443812        0       AAR  250.944411   A   \n",
       "1         27.385647           25.228976        0       LHR  185.781260   L   \n",
       "2         10.852090           12.697410        0       AAR  175.905385   A   \n",
       "3          5.536013            6.217047        1       ALU  319.823487   A   \n",
       "4         11.143211           19.200000        0       AAR  130.442161   A   \n",
       "\n",
       "   c2  c3  \n",
       "0   A   R  \n",
       "1   H   R  \n",
       "2   A   R  \n",
       "3   L   U  \n",
       "4   A   R  \n",
       "\n",
       "[5 rows x 32 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PID</th>\n      <th>county</th>\n      <th>state</th>\n      <th>area</th>\n      <th>poptotal</th>\n      <th>popdensity</th>\n      <th>popwhite</th>\n      <th>popblack</th>\n      <th>popamerindian</th>\n      <th>popasian</th>\n      <th>...</th>\n      <th>percbelowpoverty</th>\n      <th>percchildbelowpovert</th>\n      <th>percadultpoverty</th>\n      <th>percelderlypoverty</th>\n      <th>inmetro</th>\n      <th>category</th>\n      <th>dot_size</th>\n      <th>c1</th>\n      <th>c2</th>\n      <th>c3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>561</td>\n      <td>ADAMS</td>\n      <td>IL</td>\n      <td>0.052</td>\n      <td>66090</td>\n      <td>1270.961540</td>\n      <td>63917</td>\n      <td>1702</td>\n      <td>98</td>\n      <td>249</td>\n      <td>...</td>\n      <td>13.151443</td>\n      <td>18.011717</td>\n      <td>11.009776</td>\n      <td>12.443812</td>\n      <td>0</td>\n      <td>AAR</td>\n      <td>250.944411</td>\n      <td>A</td>\n      <td>A</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>562</td>\n      <td>ALEXANDER</td>\n      <td>IL</td>\n      <td>0.014</td>\n      <td>10626</td>\n      <td>759.000000</td>\n      <td>7054</td>\n      <td>3496</td>\n      <td>19</td>\n      <td>48</td>\n      <td>...</td>\n      <td>32.244278</td>\n      <td>45.826514</td>\n      <td>27.385647</td>\n      <td>25.228976</td>\n      <td>0</td>\n      <td>LHR</td>\n      <td>185.781260</td>\n      <td>L</td>\n      <td>H</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>563</td>\n      <td>BOND</td>\n      <td>IL</td>\n      <td>0.022</td>\n      <td>14991</td>\n      <td>681.409091</td>\n      <td>14477</td>\n      <td>429</td>\n      <td>35</td>\n      <td>16</td>\n      <td>...</td>\n      <td>12.068844</td>\n      <td>14.036061</td>\n      <td>10.852090</td>\n      <td>12.697410</td>\n      <td>0</td>\n      <td>AAR</td>\n      <td>175.905385</td>\n      <td>A</td>\n      <td>A</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>564</td>\n      <td>BOONE</td>\n      <td>IL</td>\n      <td>0.017</td>\n      <td>30806</td>\n      <td>1812.117650</td>\n      <td>29344</td>\n      <td>127</td>\n      <td>46</td>\n      <td>150</td>\n      <td>...</td>\n      <td>7.209019</td>\n      <td>11.179536</td>\n      <td>5.536013</td>\n      <td>6.217047</td>\n      <td>1</td>\n      <td>ALU</td>\n      <td>319.823487</td>\n      <td>A</td>\n      <td>L</td>\n      <td>U</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>565</td>\n      <td>BROWN</td>\n      <td>IL</td>\n      <td>0.018</td>\n      <td>5836</td>\n      <td>324.222222</td>\n      <td>5264</td>\n      <td>547</td>\n      <td>14</td>\n      <td>5</td>\n      <td>...</td>\n      <td>13.520249</td>\n      <td>13.022889</td>\n      <td>11.143211</td>\n      <td>19.200000</td>\n      <td>0</td>\n      <td>AAR</td>\n      <td>130.442161</td>\n      <td>A</td>\n      <td>A</td>\n      <td>R</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 32 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "midwest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder as OE\n",
    "data = midwest.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 332 entries, 0 to 331\nData columns (total 32 columns):\n #   Column                Non-Null Count  Dtype  \n---  ------                --------------  -----  \n 0   PID                   332 non-null    int64  \n 1   county                332 non-null    object \n 2   state                 332 non-null    object \n 3   area                  332 non-null    float64\n 4   poptotal              332 non-null    int64  \n 5   popdensity            332 non-null    float64\n 6   popwhite              332 non-null    int64  \n 7   popblack              332 non-null    int64  \n 8   popamerindian         332 non-null    int64  \n 9   popasian              332 non-null    int64  \n 10  popother              332 non-null    int64  \n 11  percwhite             332 non-null    float64\n 12  percblack             332 non-null    float64\n 13  percamerindan         332 non-null    float64\n 14  percasian             332 non-null    float64\n 15  percother             332 non-null    float64\n 16  popadults             332 non-null    int64  \n 17  perchsd               332 non-null    float64\n 18  percollege            332 non-null    float64\n 19  percprof              332 non-null    float64\n 20  poppovertyknown       332 non-null    int64  \n 21  percpovertyknown      332 non-null    float64\n 22  percbelowpoverty      332 non-null    float64\n 23  percchildbelowpovert  332 non-null    float64\n 24  percadultpoverty      332 non-null    float64\n 25  percelderlypoverty    332 non-null    float64\n 26  inmetro               332 non-null    int64  \n 27  category              332 non-null    object \n 28  dot_size              332 non-null    float64\n 29  c1                    332 non-null    object \n 30  c2                    332 non-null    object \n 31  c3                    332 non-null    object \ndtypes: float64(16), int64(10), object(6)\nmemory usage: 83.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 换成中文名\n",
    "data.columns = [\"城市ID\",\"郡\",\"州\",\"面积\",\"总人口\",\"人口密度\",\"白人人口\",\"非裔人口\",\"美洲印第安人人口\",\"亚洲人口\",\"其他人种人口\"\n",
    "                   ,\"白人所占比例\",\"非裔所占比例\",\"美洲印第安人所占比例\",\"亚洲人所占比例\",\"其他人种比例\"\n",
    "                   ,\"成年人口\",\"具有高中文凭的比率\",\"大学文凭比例\",\"有工作的人群比例\"\n",
    "                   ,\"已知贫困人口\",\"已知贫困人口的比例\",\"贫困线以下的人的比例\",\"贫困线以下的儿童所占比例\",\"贫困的成年人所占的比例\",\"贫困的老年人所占的比例\"\n",
    "                   ,\"是否拥有地铁\",\"标签\",\"点的尺寸\",\"c1\",\"c2\",\"c3\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去掉对象类型的列\n",
    "data = data.loc[:,data.dtypes.values != 'O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将int类型转成float类型\n",
    "data.loc[:,data.dtypes.values == 'int64'] = data.loc[:,data.dtypes.values == 'int64']*1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 332 entries, 0 to 331\nData columns (total 26 columns):\n #   Column        Non-Null Count  Dtype  \n---  ------        --------------  -----  \n 0   城市ID          332 non-null    float64\n 1   面积            332 non-null    float64\n 2   总人口           332 non-null    float64\n 3   人口密度          332 non-null    float64\n 4   白人人口          332 non-null    float64\n 5   非裔人口          332 non-null    float64\n 6   美洲印第安人人口      332 non-null    float64\n 7   亚洲人口          332 non-null    float64\n 8   其他人种人口        332 non-null    float64\n 9   白人所占比例        332 non-null    float64\n 10  非裔所占比例        332 non-null    float64\n 11  美洲印第安人所占比例    332 non-null    float64\n 12  亚洲人所占比例       332 non-null    float64\n 13  其他人种比例        332 non-null    float64\n 14  成年人口          332 non-null    float64\n 15  具有高中文凭的比率     332 non-null    float64\n 16  大学文凭比例        332 non-null    float64\n 17  有工作的人群比例      332 non-null    float64\n 18  已知贫困人口        332 non-null    float64\n 19  已知贫困人口的比例     332 non-null    float64\n 20  贫困线以下的人的比例    332 non-null    float64\n 21  贫困线以下的儿童所占比例  332 non-null    float64\n 22  贫困的成年人所占的比例   332 non-null    float64\n 23  贫困的老年人所占的比例   332 non-null    float64\n 24  是否拥有地铁        332 non-null    float64\n 25  点的尺寸          332 non-null    float64\ndtypes: float64(26)\nmemory usage: 67.6 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 城市id也没用，可以去掉\n",
    "X = data.iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      面积      总人口         人口密度     白人人口    非裔人口  美洲印第安人人口   亚洲人口  其他人种人口  \\\n",
       "0  0.052  66090.0  1270.961540  63917.0  1702.0      98.0  249.0   124.0   \n",
       "1  0.014  10626.0   759.000000   7054.0  3496.0      19.0   48.0     9.0   \n",
       "2  0.022  14991.0   681.409091  14477.0   429.0      35.0   16.0    34.0   \n",
       "3  0.017  30806.0  1812.117650  29344.0   127.0      46.0  150.0  1139.0   \n",
       "4  0.018   5836.0   324.222222   5264.0   547.0      14.0    5.0     6.0   \n",
       "\n",
       "      白人所占比例     非裔所占比例  ...     大学文凭比例  有工作的人群比例   已知贫困人口  已知贫困人口的比例  \\\n",
       "0  96.712059   2.575276  ...  19.631392  4.355859  63628.0  96.274777   \n",
       "1  66.384340  32.900433  ...  11.243308  2.870315  10529.0  99.087145   \n",
       "2  96.571276   2.861717  ...  17.033819  4.488572  14235.0  94.956974   \n",
       "3  95.254171   0.412257  ...  17.278954  4.197800  30337.0  98.477569   \n",
       "4  90.198766   9.372858  ...  14.475999  3.367680   4815.0  82.505140   \n",
       "\n",
       "   贫困线以下的人的比例  贫困线以下的儿童所占比例  贫困的成年人所占的比例  贫困的老年人所占的比例  是否拥有地铁        点的尺寸  \n",
       "0   13.151443     18.011717    11.009776    12.443812     0.0  250.944411  \n",
       "1   32.244278     45.826514    27.385647    25.228976     0.0  185.781260  \n",
       "2   12.068844     14.036061    10.852090    12.697410     0.0  175.905385  \n",
       "3    7.209019     11.179536     5.536013     6.217047     1.0  319.823487  \n",
       "4   13.520249     13.022889    11.143211    19.200000     0.0  130.442161  \n",
       "\n",
       "[5 rows x 25 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>面积</th>\n      <th>总人口</th>\n      <th>人口密度</th>\n      <th>白人人口</th>\n      <th>非裔人口</th>\n      <th>美洲印第安人人口</th>\n      <th>亚洲人口</th>\n      <th>其他人种人口</th>\n      <th>白人所占比例</th>\n      <th>非裔所占比例</th>\n      <th>...</th>\n      <th>大学文凭比例</th>\n      <th>有工作的人群比例</th>\n      <th>已知贫困人口</th>\n      <th>已知贫困人口的比例</th>\n      <th>贫困线以下的人的比例</th>\n      <th>贫困线以下的儿童所占比例</th>\n      <th>贫困的成年人所占的比例</th>\n      <th>贫困的老年人所占的比例</th>\n      <th>是否拥有地铁</th>\n      <th>点的尺寸</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.052</td>\n      <td>66090.0</td>\n      <td>1270.961540</td>\n      <td>63917.0</td>\n      <td>1702.0</td>\n      <td>98.0</td>\n      <td>249.0</td>\n      <td>124.0</td>\n      <td>96.712059</td>\n      <td>2.575276</td>\n      <td>...</td>\n      <td>19.631392</td>\n      <td>4.355859</td>\n      <td>63628.0</td>\n      <td>96.274777</td>\n      <td>13.151443</td>\n      <td>18.011717</td>\n      <td>11.009776</td>\n      <td>12.443812</td>\n      <td>0.0</td>\n      <td>250.944411</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.014</td>\n      <td>10626.0</td>\n      <td>759.000000</td>\n      <td>7054.0</td>\n      <td>3496.0</td>\n      <td>19.0</td>\n      <td>48.0</td>\n      <td>9.0</td>\n      <td>66.384340</td>\n      <td>32.900433</td>\n      <td>...</td>\n      <td>11.243308</td>\n      <td>2.870315</td>\n      <td>10529.0</td>\n      <td>99.087145</td>\n      <td>32.244278</td>\n      <td>45.826514</td>\n      <td>27.385647</td>\n      <td>25.228976</td>\n      <td>0.0</td>\n      <td>185.781260</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.022</td>\n      <td>14991.0</td>\n      <td>681.409091</td>\n      <td>14477.0</td>\n      <td>429.0</td>\n      <td>35.0</td>\n      <td>16.0</td>\n      <td>34.0</td>\n      <td>96.571276</td>\n      <td>2.861717</td>\n      <td>...</td>\n      <td>17.033819</td>\n      <td>4.488572</td>\n      <td>14235.0</td>\n      <td>94.956974</td>\n      <td>12.068844</td>\n      <td>14.036061</td>\n      <td>10.852090</td>\n      <td>12.697410</td>\n      <td>0.0</td>\n      <td>175.905385</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.017</td>\n      <td>30806.0</td>\n      <td>1812.117650</td>\n      <td>29344.0</td>\n      <td>127.0</td>\n      <td>46.0</td>\n      <td>150.0</td>\n      <td>1139.0</td>\n      <td>95.254171</td>\n      <td>0.412257</td>\n      <td>...</td>\n      <td>17.278954</td>\n      <td>4.197800</td>\n      <td>30337.0</td>\n      <td>98.477569</td>\n      <td>7.209019</td>\n      <td>11.179536</td>\n      <td>5.536013</td>\n      <td>6.217047</td>\n      <td>1.0</td>\n      <td>319.823487</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.018</td>\n      <td>5836.0</td>\n      <td>324.222222</td>\n      <td>5264.0</td>\n      <td>547.0</td>\n      <td>14.0</td>\n      <td>5.0</td>\n      <td>6.0</td>\n      <td>90.198766</td>\n      <td>9.372858</td>\n      <td>...</td>\n      <td>14.475999</td>\n      <td>3.367680</td>\n      <td>4815.0</td>\n      <td>82.505140</td>\n      <td>13.520249</td>\n      <td>13.022889</td>\n      <td>11.143211</td>\n      <td>19.200000</td>\n      <td>0.0</td>\n      <td>130.442161</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 25 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  c1 c2 c3\n",
       "0  A  A  R\n",
       "1  L  H  R\n",
       "2  A  A  R\n",
       "3  A  L  U\n",
       "4  A  A  R"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>c1</th>\n      <th>c2</th>\n      <th>c3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>A</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>L</td>\n      <td>H</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A</td>\n      <td>A</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A</td>\n      <td>L</td>\n      <td>U</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A</td>\n      <td>A</td>\n      <td>R</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "y = midwest.iloc[:, -3:]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分训练集和测试集\n",
    "from sklearn.model_selection import train_test_split as TTS\n",
    "Xtrain, Xtest, Ytrain, Ytest = TTS(X, y, test_size=0.3, random_state=420)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        面积      总人口         人口密度     白人人口    非裔人口  美洲印第安人人口    亚洲人口  其他人种人口  \\\n",
       "276  0.041  15682.0   382.487805  15001.0   375.0     125.0    56.0   125.0   \n",
       "156  0.030  23265.0   775.500000  23127.0     2.0      50.0    41.0    45.0   \n",
       "35   0.036  61067.0  1696.305560  51991.0  6342.0     109.0  2178.0   447.0   \n",
       "161  0.031  18185.0   586.612903  17895.0    23.0     211.0    24.0    32.0   \n",
       "321  0.079  14181.0   179.506329  11962.0    18.0    2167.0    15.0    19.0   \n",
       "\n",
       "        白人所占比例     非裔所占比例  ...     大学文凭比例   有工作的人群比例   已知贫困人口  已知贫困人口的比例  \\\n",
       "276  95.657442   2.391277  ...  12.427492   2.390578  14534.0  92.679505   \n",
       "156  99.406834   0.008597  ...  16.583835   5.166100  22974.0  98.749194   \n",
       "35   85.137636  10.385314  ...  36.643665  14.089892  54230.0  88.804100   \n",
       "161  98.405279   0.126478  ...  19.039803   5.014362  17942.0  98.663734   \n",
       "321  84.352302   0.126930  ...  17.947917   4.041667  13897.0  97.997320   \n",
       "\n",
       "     贫困线以下的人的比例  贫困线以下的儿童所占比例  贫困的成年人所占的比例  贫困的老年人所占的比例  是否拥有地铁        点的尺寸  \n",
       "276   14.435118     22.300831    13.310056    10.186757     0.0  137.858283  \n",
       "156    7.704361      8.136567     6.168723    11.040340     0.0  187.881402  \n",
       "35    28.371750     26.392211    32.458483    13.815301     0.0  305.082769  \n",
       "161   13.214803     18.758142    12.037542     9.681284     0.0  163.839599  \n",
       "321   20.544002     29.073570    19.195368    14.179318     0.0  112.022528  \n",
       "\n",
       "[5 rows x 25 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>面积</th>\n      <th>总人口</th>\n      <th>人口密度</th>\n      <th>白人人口</th>\n      <th>非裔人口</th>\n      <th>美洲印第安人人口</th>\n      <th>亚洲人口</th>\n      <th>其他人种人口</th>\n      <th>白人所占比例</th>\n      <th>非裔所占比例</th>\n      <th>...</th>\n      <th>大学文凭比例</th>\n      <th>有工作的人群比例</th>\n      <th>已知贫困人口</th>\n      <th>已知贫困人口的比例</th>\n      <th>贫困线以下的人的比例</th>\n      <th>贫困线以下的儿童所占比例</th>\n      <th>贫困的成年人所占的比例</th>\n      <th>贫困的老年人所占的比例</th>\n      <th>是否拥有地铁</th>\n      <th>点的尺寸</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>276</th>\n      <td>0.041</td>\n      <td>15682.0</td>\n      <td>382.487805</td>\n      <td>15001.0</td>\n      <td>375.0</td>\n      <td>125.0</td>\n      <td>56.0</td>\n      <td>125.0</td>\n      <td>95.657442</td>\n      <td>2.391277</td>\n      <td>...</td>\n      <td>12.427492</td>\n      <td>2.390578</td>\n      <td>14534.0</td>\n      <td>92.679505</td>\n      <td>14.435118</td>\n      <td>22.300831</td>\n      <td>13.310056</td>\n      <td>10.186757</td>\n      <td>0.0</td>\n      <td>137.858283</td>\n    </tr>\n    <tr>\n      <th>156</th>\n      <td>0.030</td>\n      <td>23265.0</td>\n      <td>775.500000</td>\n      <td>23127.0</td>\n      <td>2.0</td>\n      <td>50.0</td>\n      <td>41.0</td>\n      <td>45.0</td>\n      <td>99.406834</td>\n      <td>0.008597</td>\n      <td>...</td>\n      <td>16.583835</td>\n      <td>5.166100</td>\n      <td>22974.0</td>\n      <td>98.749194</td>\n      <td>7.704361</td>\n      <td>8.136567</td>\n      <td>6.168723</td>\n      <td>11.040340</td>\n      <td>0.0</td>\n      <td>187.881402</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>0.036</td>\n      <td>61067.0</td>\n      <td>1696.305560</td>\n      <td>51991.0</td>\n      <td>6342.0</td>\n      <td>109.0</td>\n      <td>2178.0</td>\n      <td>447.0</td>\n      <td>85.137636</td>\n      <td>10.385314</td>\n      <td>...</td>\n      <td>36.643665</td>\n      <td>14.089892</td>\n      <td>54230.0</td>\n      <td>88.804100</td>\n      <td>28.371750</td>\n      <td>26.392211</td>\n      <td>32.458483</td>\n      <td>13.815301</td>\n      <td>0.0</td>\n      <td>305.082769</td>\n    </tr>\n    <tr>\n      <th>161</th>\n      <td>0.031</td>\n      <td>18185.0</td>\n      <td>586.612903</td>\n      <td>17895.0</td>\n      <td>23.0</td>\n      <td>211.0</td>\n      <td>24.0</td>\n      <td>32.0</td>\n      <td>98.405279</td>\n      <td>0.126478</td>\n      <td>...</td>\n      <td>19.039803</td>\n      <td>5.014362</td>\n      <td>17942.0</td>\n      <td>98.663734</td>\n      <td>13.214803</td>\n      <td>18.758142</td>\n      <td>12.037542</td>\n      <td>9.681284</td>\n      <td>0.0</td>\n      <td>163.839599</td>\n    </tr>\n    <tr>\n      <th>321</th>\n      <td>0.079</td>\n      <td>14181.0</td>\n      <td>179.506329</td>\n      <td>11962.0</td>\n      <td>18.0</td>\n      <td>2167.0</td>\n      <td>15.0</td>\n      <td>19.0</td>\n      <td>84.352302</td>\n      <td>0.126930</td>\n      <td>...</td>\n      <td>17.947917</td>\n      <td>4.041667</td>\n      <td>13897.0</td>\n      <td>97.997320</td>\n      <td>20.544002</td>\n      <td>29.073570</td>\n      <td>19.195368</td>\n      <td>14.179318</td>\n      <td>0.0</td>\n      <td>112.022528</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 25 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "Xtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 恢复索引\n",
    "Xtrain.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtest.reset_index(inplace=True)\n",
    "Ytrain.reset_index(inplace=True)\n",
    "Ytest.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标准化数据集，标准化前去掉“是否拥有地跌”\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler().fit(Xtrain.iloc[:,[*range(23), -1]])  # 以训练集为标准的均值和方差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain_ = Xtrain.copy()\n",
    "Xtest_ = Xtest.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain_.iloc[:,[*range(23), -1]] = ss.transform(Xtrain.iloc[:,[*range(23), -1]])\n",
    "Xtest_.iloc[:,[*range(23), -1]] = ss.transform(Xtest.iloc[:,[*range(23), -1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain_ = Xtrain_.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         面积       总人口      人口密度      白人人口      非裔人口  美洲印第安人人口      亚洲人口  \\\n",
       "0  0.616975 -0.781589 -0.817515 -0.806262 -0.109249 -0.024001 -0.298458   \n",
       "1 -0.141464 -0.374282 -0.347133 -0.350269 -0.486592 -0.330845 -0.368263   \n",
       "2  0.272230  1.656180  0.754945  1.269442  5.927225 -0.089461  9.576664   \n",
       "3 -0.072515 -0.647145 -0.573205 -0.643865 -0.465347  0.327847 -0.447376   \n",
       "4  3.237036 -0.862212 -1.060456 -0.976797 -0.470405  8.330347 -0.489259   \n",
       "\n",
       "     其他人种人口    白人所占比例    非裔所占比例  ...    大学文凭比例  有工作的人群比例    已知贫困人口  已知贫困人口的比例  \\\n",
       "0 -0.020924 -0.560519  0.359059  ... -0.879865 -0.848804 -0.824188  -1.455058   \n",
       "1 -0.341073  0.588691 -0.457448  ... -0.002157  0.773189 -0.351151   0.550676   \n",
       "2  1.267678 -3.784901  3.098491  ...  4.233941  5.988182  1.400655  -2.735690   \n",
       "3 -0.393097  0.281709 -0.417052  ...  0.516478  0.684514 -0.633180   0.522436   \n",
       "4 -0.445122 -4.025610 -0.416897  ...  0.285901  0.116079 -0.859890   0.302219   \n",
       "\n",
       "   贫困线以下的人的比例  贫困线以下的儿童所占比例  贫困的成年人所占的比例  贫困的老年人所占的比例  是否拥有地铁      点的尺寸  \n",
       "0    0.342460      0.839258     0.428156    10.186757     0.0 -0.817515  \n",
       "1   -1.033914     -1.207832    -0.982892    11.040340     0.0 -0.347133  \n",
       "2    3.192365      1.430564     4.211672    13.815301     0.0  0.754945  \n",
       "3    0.092918      0.327251     0.176721     9.681284     0.0 -0.573205  \n",
       "4    1.591667      1.818087     1.591028    14.179318     0.0 -1.060456  \n",
       "\n",
       "[5 rows x 25 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>面积</th>\n      <th>总人口</th>\n      <th>人口密度</th>\n      <th>白人人口</th>\n      <th>非裔人口</th>\n      <th>美洲印第安人人口</th>\n      <th>亚洲人口</th>\n      <th>其他人种人口</th>\n      <th>白人所占比例</th>\n      <th>非裔所占比例</th>\n      <th>...</th>\n      <th>大学文凭比例</th>\n      <th>有工作的人群比例</th>\n      <th>已知贫困人口</th>\n      <th>已知贫困人口的比例</th>\n      <th>贫困线以下的人的比例</th>\n      <th>贫困线以下的儿童所占比例</th>\n      <th>贫困的成年人所占的比例</th>\n      <th>贫困的老年人所占的比例</th>\n      <th>是否拥有地铁</th>\n      <th>点的尺寸</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.616975</td>\n      <td>-0.781589</td>\n      <td>-0.817515</td>\n      <td>-0.806262</td>\n      <td>-0.109249</td>\n      <td>-0.024001</td>\n      <td>-0.298458</td>\n      <td>-0.020924</td>\n      <td>-0.560519</td>\n      <td>0.359059</td>\n      <td>...</td>\n      <td>-0.879865</td>\n      <td>-0.848804</td>\n      <td>-0.824188</td>\n      <td>-1.455058</td>\n      <td>0.342460</td>\n      <td>0.839258</td>\n      <td>0.428156</td>\n      <td>10.186757</td>\n      <td>0.0</td>\n      <td>-0.817515</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.141464</td>\n      <td>-0.374282</td>\n      <td>-0.347133</td>\n      <td>-0.350269</td>\n      <td>-0.486592</td>\n      <td>-0.330845</td>\n      <td>-0.368263</td>\n      <td>-0.341073</td>\n      <td>0.588691</td>\n      <td>-0.457448</td>\n      <td>...</td>\n      <td>-0.002157</td>\n      <td>0.773189</td>\n      <td>-0.351151</td>\n      <td>0.550676</td>\n      <td>-1.033914</td>\n      <td>-1.207832</td>\n      <td>-0.982892</td>\n      <td>11.040340</td>\n      <td>0.0</td>\n      <td>-0.347133</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.272230</td>\n      <td>1.656180</td>\n      <td>0.754945</td>\n      <td>1.269442</td>\n      <td>5.927225</td>\n      <td>-0.089461</td>\n      <td>9.576664</td>\n      <td>1.267678</td>\n      <td>-3.784901</td>\n      <td>3.098491</td>\n      <td>...</td>\n      <td>4.233941</td>\n      <td>5.988182</td>\n      <td>1.400655</td>\n      <td>-2.735690</td>\n      <td>3.192365</td>\n      <td>1.430564</td>\n      <td>4.211672</td>\n      <td>13.815301</td>\n      <td>0.0</td>\n      <td>0.754945</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.072515</td>\n      <td>-0.647145</td>\n      <td>-0.573205</td>\n      <td>-0.643865</td>\n      <td>-0.465347</td>\n      <td>0.327847</td>\n      <td>-0.447376</td>\n      <td>-0.393097</td>\n      <td>0.281709</td>\n      <td>-0.417052</td>\n      <td>...</td>\n      <td>0.516478</td>\n      <td>0.684514</td>\n      <td>-0.633180</td>\n      <td>0.522436</td>\n      <td>0.092918</td>\n      <td>0.327251</td>\n      <td>0.176721</td>\n      <td>9.681284</td>\n      <td>0.0</td>\n      <td>-0.573205</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3.237036</td>\n      <td>-0.862212</td>\n      <td>-1.060456</td>\n      <td>-0.976797</td>\n      <td>-0.470405</td>\n      <td>8.330347</td>\n      <td>-0.489259</td>\n      <td>-0.445122</td>\n      <td>-4.025610</td>\n      <td>-0.416897</td>\n      <td>...</td>\n      <td>0.285901</td>\n      <td>0.116079</td>\n      <td>-0.859890</td>\n      <td>0.302219</td>\n      <td>1.591667</td>\n      <td>1.818087</td>\n      <td>1.591028</td>\n      <td>14.179318</td>\n      <td>0.0</td>\n      <td>-1.060456</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 25 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "Xtrain_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         面积       总人口      人口密度      白人人口      非裔人口   美洲印第安人人口      亚洲人口  \\\n",
       "0  2.616496 -0.916248 -1.050034 -0.916978 -0.484568  -0.118100 -0.410146   \n",
       "1 -0.762005 -1.414974 -1.053596 -1.624704 -0.488615  13.657164 -0.559064   \n",
       "2 -0.693056  1.795296  2.187823  1.818418  0.528086  -0.138556  2.279684   \n",
       "3  1.582261 -1.314369 -1.149891 -1.344015 -0.486592   0.818798 -0.531142   \n",
       "4 -0.141464  1.828384  1.288899  1.888281 -0.226599   1.735240  0.920808   \n",
       "\n",
       "     其他人种人口     白人所占比例    非裔所占比例  ...    大学文凭比例  有工作的人群比例    已知贫困人口  \\\n",
       "0 -0.485140   0.428516 -0.449990  ... -0.239992 -0.327103 -0.916553   \n",
       "1 -0.501148 -26.602282 -0.460394  ... -1.955029 -1.941785 -1.424676   \n",
       "2  0.163162  -0.136157  0.080627  ...  1.320417  1.753797  1.880081   \n",
       "3 -0.497146  -1.064386 -0.448501  ... -0.462127 -0.298376 -1.326818   \n",
       "4 -0.032929   0.172491 -0.322303  ...  3.036940  1.859724  1.885013   \n",
       "\n",
       "   已知贫困人口的比例  贫困线以下的人的比例  贫困线以下的儿童所占比例  贫困的成年人所占的比例  贫困的老年人所占的比例  是否拥有地铁  \\\n",
       "0   0.239146    0.891360      0.987149     0.654614    16.512236     0.0   \n",
       "1   0.369364    7.347473      6.910410     6.356297    18.218623     0.0   \n",
       "2   0.510822   -0.861977     -0.832025    -0.820138    10.811943     0.0   \n",
       "3  -0.165594    1.005754      0.932132     1.132860    12.835093     0.0   \n",
       "4   0.243701   -0.861170     -0.764387    -0.720235     7.691539     0.0   \n",
       "\n",
       "       点的尺寸  \n",
       "0 -1.050034  \n",
       "1 -1.053596  \n",
       "2  2.187823  \n",
       "3 -1.149891  \n",
       "4  1.288899  \n",
       "\n",
       "[5 rows x 25 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>面积</th>\n      <th>总人口</th>\n      <th>人口密度</th>\n      <th>白人人口</th>\n      <th>非裔人口</th>\n      <th>美洲印第安人人口</th>\n      <th>亚洲人口</th>\n      <th>其他人种人口</th>\n      <th>白人所占比例</th>\n      <th>非裔所占比例</th>\n      <th>...</th>\n      <th>大学文凭比例</th>\n      <th>有工作的人群比例</th>\n      <th>已知贫困人口</th>\n      <th>已知贫困人口的比例</th>\n      <th>贫困线以下的人的比例</th>\n      <th>贫困线以下的儿童所占比例</th>\n      <th>贫困的成年人所占的比例</th>\n      <th>贫困的老年人所占的比例</th>\n      <th>是否拥有地铁</th>\n      <th>点的尺寸</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.616496</td>\n      <td>-0.916248</td>\n      <td>-1.050034</td>\n      <td>-0.916978</td>\n      <td>-0.484568</td>\n      <td>-0.118100</td>\n      <td>-0.410146</td>\n      <td>-0.485140</td>\n      <td>0.428516</td>\n      <td>-0.449990</td>\n      <td>...</td>\n      <td>-0.239992</td>\n      <td>-0.327103</td>\n      <td>-0.916553</td>\n      <td>0.239146</td>\n      <td>0.891360</td>\n      <td>0.987149</td>\n      <td>0.654614</td>\n      <td>16.512236</td>\n      <td>0.0</td>\n      <td>-1.050034</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.762005</td>\n      <td>-1.414974</td>\n      <td>-1.053596</td>\n      <td>-1.624704</td>\n      <td>-0.488615</td>\n      <td>13.657164</td>\n      <td>-0.559064</td>\n      <td>-0.501148</td>\n      <td>-26.602282</td>\n      <td>-0.460394</td>\n      <td>...</td>\n      <td>-1.955029</td>\n      <td>-1.941785</td>\n      <td>-1.424676</td>\n      <td>0.369364</td>\n      <td>7.347473</td>\n      <td>6.910410</td>\n      <td>6.356297</td>\n      <td>18.218623</td>\n      <td>0.0</td>\n      <td>-1.053596</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.693056</td>\n      <td>1.795296</td>\n      <td>2.187823</td>\n      <td>1.818418</td>\n      <td>0.528086</td>\n      <td>-0.138556</td>\n      <td>2.279684</td>\n      <td>0.163162</td>\n      <td>-0.136157</td>\n      <td>0.080627</td>\n      <td>...</td>\n      <td>1.320417</td>\n      <td>1.753797</td>\n      <td>1.880081</td>\n      <td>0.510822</td>\n      <td>-0.861977</td>\n      <td>-0.832025</td>\n      <td>-0.820138</td>\n      <td>10.811943</td>\n      <td>0.0</td>\n      <td>2.187823</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.582261</td>\n      <td>-1.314369</td>\n      <td>-1.149891</td>\n      <td>-1.344015</td>\n      <td>-0.486592</td>\n      <td>0.818798</td>\n      <td>-0.531142</td>\n      <td>-0.497146</td>\n      <td>-1.064386</td>\n      <td>-0.448501</td>\n      <td>...</td>\n      <td>-0.462127</td>\n      <td>-0.298376</td>\n      <td>-1.326818</td>\n      <td>-0.165594</td>\n      <td>1.005754</td>\n      <td>0.932132</td>\n      <td>1.132860</td>\n      <td>12.835093</td>\n      <td>0.0</td>\n      <td>-1.149891</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-0.141464</td>\n      <td>1.828384</td>\n      <td>1.288899</td>\n      <td>1.888281</td>\n      <td>-0.226599</td>\n      <td>1.735240</td>\n      <td>0.920808</td>\n      <td>-0.032929</td>\n      <td>0.172491</td>\n      <td>-0.322303</td>\n      <td>...</td>\n      <td>3.036940</td>\n      <td>1.859724</td>\n      <td>1.885013</td>\n      <td>0.243701</td>\n      <td>-0.861170</td>\n      <td>-0.764387</td>\n      <td>-0.720235</td>\n      <td>7.691539</td>\n      <td>0.0</td>\n      <td>1.288899</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 25 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "Xtest_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtest_ = Xtest_.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array(['L', 'A', 'A', 'A', 'A', 'A', 'A', 'L', 'A', 'A', 'A', 'H', 'A',\n",
       "       'A', 'A', 'A', 'L', 'L', 'A', 'L', 'A', 'A', 'A', 'A', 'A', 'L',\n",
       "       'L', 'A', 'A', 'L', 'A', 'A', 'A', 'L', 'A', 'A', 'A', 'A', 'A',\n",
       "       'L', 'L', 'A', 'A', 'A', 'L', 'A', 'A', 'L', 'A', 'A', 'A', 'A',\n",
       "       'A', 'A', 'A', 'A', 'A', 'L', 'A', 'L', 'A', 'L', 'A', 'A', 'L',\n",
       "       'A', 'A', 'A', 'L', 'L', 'A', 'A', 'A', 'H', 'A', 'A', 'A', 'A',\n",
       "       'A', 'L', 'A', 'A', 'A', 'A', 'H', 'L', 'A', 'A', 'A', 'L', 'A',\n",
       "       'A', 'A', 'A', 'L', 'A', 'A', 'A', 'H', 'A', 'A', 'A', 'A', 'L',\n",
       "       'L', 'A', 'A', 'L', 'A', 'L', 'A', 'A', 'A', 'A', 'L', 'A', 'A',\n",
       "       'A', 'H', 'A', 'A', 'A', 'A', 'A', 'A', 'L', 'L', 'A', 'A', 'H',\n",
       "       'A', 'A', 'H', 'A', 'A', 'H', 'H', 'A', 'L', 'A', 'A', 'A', 'L',\n",
       "       'L', 'A', 'A', 'A', 'A', 'A', 'H', 'A', 'A', 'A', 'A', 'A', 'A',\n",
       "       'A', 'A', 'A', 'A', 'L', 'A', 'L', 'L', 'A', 'H', 'A', 'A', 'A',\n",
       "       'A', 'L', 'A', 'L', 'A', 'A', 'A', 'A', 'L', 'A', 'A', 'H', 'A',\n",
       "       'L', 'A', 'L', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'H',\n",
       "       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',\n",
       "       'H', 'L', 'A', 'H', 'A', 'A', 'A', 'A', 'A', 'L', 'H', 'A', 'A',\n",
       "       'A', 'A', 'A', 'A', 'L', 'A', 'A', 'A', 'A', 'L', 'A'],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "source": [
    "Ytrain.iloc[:,1].ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  c1 c2 c3\n",
       "0  L  A  R\n",
       "1  A  A  R\n",
       "2  A  H  R\n",
       "3  A  A  R\n",
       "4  A  H  R"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>c1</th>\n      <th>c2</th>\n      <th>c3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>L</td>\n      <td>A</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A</td>\n      <td>A</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A</td>\n      <td>H</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A</td>\n      <td>A</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A</td>\n      <td>H</td>\n      <td>R</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "Ytrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ytrain = Ytrain.iloc[:,1:]\n",
    "Ytest = Ytest.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain = Xtrain.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      面积      总人口         人口密度     白人人口    非裔人口  美洲印第安人人口    亚洲人口  其他人种人口  \\\n",
       "0  0.041  15682.0   382.487805  15001.0   375.0     125.0    56.0   125.0   \n",
       "1  0.030  23265.0   775.500000  23127.0     2.0      50.0    41.0    45.0   \n",
       "2  0.036  61067.0  1696.305560  51991.0  6342.0     109.0  2178.0   447.0   \n",
       "3  0.031  18185.0   586.612903  17895.0    23.0     211.0    24.0    32.0   \n",
       "4  0.079  14181.0   179.506329  11962.0    18.0    2167.0    15.0    19.0   \n",
       "\n",
       "      白人所占比例     非裔所占比例  ...     大学文凭比例   有工作的人群比例   已知贫困人口  已知贫困人口的比例  \\\n",
       "0  95.657442   2.391277  ...  12.427492   2.390578  14534.0  92.679505   \n",
       "1  99.406834   0.008597  ...  16.583835   5.166100  22974.0  98.749194   \n",
       "2  85.137636  10.385314  ...  36.643665  14.089892  54230.0  88.804100   \n",
       "3  98.405279   0.126478  ...  19.039803   5.014362  17942.0  98.663734   \n",
       "4  84.352302   0.126930  ...  17.947917   4.041667  13897.0  97.997320   \n",
       "\n",
       "   贫困线以下的人的比例  贫困线以下的儿童所占比例  贫困的成年人所占的比例  贫困的老年人所占的比例  是否拥有地铁        点的尺寸  \n",
       "0   14.435118     22.300831    13.310056    10.186757     0.0  137.858283  \n",
       "1    7.704361      8.136567     6.168723    11.040340     0.0  187.881402  \n",
       "2   28.371750     26.392211    32.458483    13.815301     0.0  305.082769  \n",
       "3   13.214803     18.758142    12.037542     9.681284     0.0  163.839599  \n",
       "4   20.544002     29.073570    19.195368    14.179318     0.0  112.022528  \n",
       "\n",
       "[5 rows x 25 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>面积</th>\n      <th>总人口</th>\n      <th>人口密度</th>\n      <th>白人人口</th>\n      <th>非裔人口</th>\n      <th>美洲印第安人人口</th>\n      <th>亚洲人口</th>\n      <th>其他人种人口</th>\n      <th>白人所占比例</th>\n      <th>非裔所占比例</th>\n      <th>...</th>\n      <th>大学文凭比例</th>\n      <th>有工作的人群比例</th>\n      <th>已知贫困人口</th>\n      <th>已知贫困人口的比例</th>\n      <th>贫困线以下的人的比例</th>\n      <th>贫困线以下的儿童所占比例</th>\n      <th>贫困的成年人所占的比例</th>\n      <th>贫困的老年人所占的比例</th>\n      <th>是否拥有地铁</th>\n      <th>点的尺寸</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.041</td>\n      <td>15682.0</td>\n      <td>382.487805</td>\n      <td>15001.0</td>\n      <td>375.0</td>\n      <td>125.0</td>\n      <td>56.0</td>\n      <td>125.0</td>\n      <td>95.657442</td>\n      <td>2.391277</td>\n      <td>...</td>\n      <td>12.427492</td>\n      <td>2.390578</td>\n      <td>14534.0</td>\n      <td>92.679505</td>\n      <td>14.435118</td>\n      <td>22.300831</td>\n      <td>13.310056</td>\n      <td>10.186757</td>\n      <td>0.0</td>\n      <td>137.858283</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.030</td>\n      <td>23265.0</td>\n      <td>775.500000</td>\n      <td>23127.0</td>\n      <td>2.0</td>\n      <td>50.0</td>\n      <td>41.0</td>\n      <td>45.0</td>\n      <td>99.406834</td>\n      <td>0.008597</td>\n      <td>...</td>\n      <td>16.583835</td>\n      <td>5.166100</td>\n      <td>22974.0</td>\n      <td>98.749194</td>\n      <td>7.704361</td>\n      <td>8.136567</td>\n      <td>6.168723</td>\n      <td>11.040340</td>\n      <td>0.0</td>\n      <td>187.881402</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.036</td>\n      <td>61067.0</td>\n      <td>1696.305560</td>\n      <td>51991.0</td>\n      <td>6342.0</td>\n      <td>109.0</td>\n      <td>2178.0</td>\n      <td>447.0</td>\n      <td>85.137636</td>\n      <td>10.385314</td>\n      <td>...</td>\n      <td>36.643665</td>\n      <td>14.089892</td>\n      <td>54230.0</td>\n      <td>88.804100</td>\n      <td>28.371750</td>\n      <td>26.392211</td>\n      <td>32.458483</td>\n      <td>13.815301</td>\n      <td>0.0</td>\n      <td>305.082769</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.031</td>\n      <td>18185.0</td>\n      <td>586.612903</td>\n      <td>17895.0</td>\n      <td>23.0</td>\n      <td>211.0</td>\n      <td>24.0</td>\n      <td>32.0</td>\n      <td>98.405279</td>\n      <td>0.126478</td>\n      <td>...</td>\n      <td>19.039803</td>\n      <td>5.014362</td>\n      <td>17942.0</td>\n      <td>98.663734</td>\n      <td>13.214803</td>\n      <td>18.758142</td>\n      <td>12.037542</td>\n      <td>9.681284</td>\n      <td>0.0</td>\n      <td>163.839599</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.079</td>\n      <td>14181.0</td>\n      <td>179.506329</td>\n      <td>11962.0</td>\n      <td>18.0</td>\n      <td>2167.0</td>\n      <td>15.0</td>\n      <td>19.0</td>\n      <td>84.352302</td>\n      <td>0.126930</td>\n      <td>...</td>\n      <td>17.947917</td>\n      <td>4.041667</td>\n      <td>13897.0</td>\n      <td>97.997320</td>\n      <td>20.544002</td>\n      <td>29.073570</td>\n      <td>19.195368</td>\n      <td>14.179318</td>\n      <td>0.0</td>\n      <td>112.022528</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 25 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "Xtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "c1\n\tTrain:0.9956896551724138\n\tTest:0.97\n     Average      High       Low      mean         name\n14  1.270841  2.190459 -3.461301  2.307534    具有高中文凭的比率\n15 -0.651762  1.379946 -0.728184  0.919964       大学文凭比例\n16 -0.364258  0.898980 -0.534722  0.599320     有工作的人群比例\n21  0.173957 -0.646609  0.472652  0.431073  贫困的成年人所占的比例\n23 -0.526989  0.343680  0.183310  0.351326       是否拥有地铁\n\t\nc2\n\tTrain:0.9827586206896551\n\tTest:0.98\n     Average      High       Low      mean          name\n20  0.213172  1.735800 -1.948972  1.299315  贫困线以下的儿童所占比例\n21 -0.296637  1.747433 -1.450796  1.164955   贫困的成年人所占的比例\n19  0.083393  1.564370 -1.647763  1.098509    贫困线以下的人的比例\n12 -0.533872  0.273615  0.260256  0.355914        其他人种比例\n23 -0.452561 -0.067895  0.520456  0.346971        是否拥有地铁\n\t\nc3\n\tTrain:1.0\n\tTest:1.0\n        Coef    name\n23  2.981828  是否拥有地铁\n3   0.103905    白人人口\n17  0.103751  已知贫困人口\n1   0.095630     总人口\n8   0.072366  白人所占比例\n\t\n"
     ]
    }
   ],
   "source": [
    "# 使用逻辑回归探索数据集\n",
    "from sklearn.linear_model import LogisticRegression as logiR\n",
    "\n",
    "for i in range(3): # 三次建模，对应c1 c2 c3\n",
    "    logi = logiR(solver=\"newton-cg\", max_iter=100**20, multi_class=\"multinomial\").fit(Xtrain_, Ytrain.iloc[:,i].ravel())\n",
    "    print(y.columns[i])\n",
    "    print(\"\\tTrain:{}\".format(logi.score(Xtrain_, Ytrain.iloc[:,i].ravel()))) # 模型的学习能力\n",
    "    print(\"\\tTest:{}\".format(logi.score(Xtest_, Ytest.iloc[:,i].ravel())))    # 模型的泛化能力\n",
    "    coeff = pd.DataFrame(logi.coef_).T \n",
    "    if i !=2 :\n",
    "        coeff['mean'] = abs(coeff).mean(axis=1)\n",
    "        coeff['name'] = Xtrain.columns\n",
    "        coeff.columns = ['Average', 'High', 'Low', 'mean','name']\n",
    "        coeff = coeff.sort_values(by='mean', ascending=False).head()\n",
    "    else:\n",
    "        coeff['name'] = Xtrain.columns\n",
    "        coeff.columns = ['Coef', 'name']\n",
    "        coeff = coeff.sort_values(by='Coef', ascending=False).head()\n",
    "    print(coeff)\n",
    "    print('\\t')"
   ]
  },
  {
   "source": [
    "结论：<br>\n",
    "第一个字母是平均教育程度的高低(越高的话，我们的标签就是H)<br>\n",
    "第二个字母是贫困水平的高低（越穷的话，我们的水平就是H）<br>\n",
    "第三个字母是城市还是乡村"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 8. 圈出来想看的点"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 960x600 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure(\n",
    "    figsize=(16,10)\n",
    "    ,dpi=60\n",
    "    ,facecolor='w'\n",
    "    ,edgecolor='k'\n",
    ")\n",
    "# 循环画图\n",
    "for i, category in enumerate(categories):\n",
    "    plt.scatter(\n",
    "        'area', 'poptotal'\n",
    "        ,data=midwest.loc[midwest.category == category, :]\n",
    "        ,c = np.array(plt.cm.tab10(i/float(len(categories) - 1))).reshape(1,-1)\n",
    "        ,label=str(category)\n",
    "        )\n",
    "\n",
    "# 圈出来想要看的点\n",
    "plt.scatter(\n",
    "    'area','poptotal'\n",
    "    ,data = midwest.loc[midwest.category == \"LHR\", :]\n",
    "    ,s = 150\n",
    "    ,facecolors = 'None'    # 点的填充颜色，为None表示点是透明的\n",
    "    ,edgecolors='red'       # 点的边框颜色\n",
    "    ,label = 'Selected'\n",
    ")\n",
    "\n",
    "plt.gca().set(xlim=(0.0, 0.12), ylim=(0, 90000))\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.ylabel('Population', fontsize=22)\n",
    "plt.xlabel('Area', fontsize=22)\n",
    "plt.title(\"Scatterplot of Midwest Area vs Population\", fontsize=22)\n",
    "plt.legend(fontsize=12)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}